136 Meniscal surgery or exercise therapy in alleviating patient-reported mechanical symptoms in young adults with a meniscal tear
Bibliographic record
Abstract
Introduction A common treatment strategy to alleviate mechanical symptoms in young patients with meniscal tears is meniscal surgery, however, it is unknown whether this is superior to a non-surgical strategy. Therefore, we aimed to compare meniscal surgery to early exercise therapy and patient education. Materials and Methods In the DREAM trial 121 patients aged 18–40 were randomized to surgery (partial meniscectomy or meniscal repair) or 12-weeks of supervised exercise therapy and patient education, with the option of later surgery if needed. In this secondary analysis we included patients with self-reported mechanical symptoms (yes/no) at baseline. Patients were followed for 12 months and assessed for the presence of mechanical symptoms at 3, 6 and 12 months. Results In total, 63/121 patients reported mechanical symptoms at baseline (surgery, n=33 and exercise, n=30), while 9/26 in the surgery group and 20/29 in the exercise group reported mechanical symptoms at 12-month (missing data on 8 patients). During follow-up 8 patients crossed over from the exercise group to use the opportunity for later surgery. At 12-month the risk difference was 34.4% (95% CI 9.5–59.2) and the relative risk was 1.99 (95%CI, 1.11–3.57) in favour of the surgery group. Similarly, a larger proportion of patients in the exercise group reported mechanical symptoms at 3 and 6 months. Conclusion Our results suggest that meniscal surgery may be superior in alleviating mechanical symptoms compared with exercise therapy and patient education with the option of later surgery in young patients with meniscal tears and self-reported mechanical symptoms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".